Software traceability is a fundamentally important task in software engineering. The need for automated traceability increases as projects become more complex and as the number of artifacts increases. We propose an automated technique that combines traceability with a machine learning technique known as topic modeling. Our approach automatically records traceability links during the software development process and learns a probabilistic topic model over artifacts. The learned model allows for the semantic categorization of artifacts and the topical visualization of the software system. To test our approach, we have implemented several tools: an artifact search tool combining keyword-based search and topic modeling, a recording tool that performs prospective traceability, and a visualization tool that allows one to navigate the software architecture and view semantic topics associated with relevant artifacts and architectural components. We apply our approach to several data sets and discuss how topic modeling enhances software traceability, and vice versa.
Traceability is an important aspect of software development that is often required by various professional standards and government agencies. Yet current industrial approaches do not typically address end-to-end traceability.Moreover, many industry projects become entangled in process overhead and fail to derive much benefit from current traceability solutions. This paper presents a successful end-to-end software traceability tool developed at Wonderware, a software development company and a business unit of Invensys Systems, Inc. This process-oriented approach achieves comprehensive traceability and supports the entire software development life cycle by focusing on both requirements traceability and process traceability. We offer new perspectives in analyzing the problem as well as general traceability guidelines. These guidelines have emerged from the experience of implementing and deploying the traceability tool within actual company constraints. We discuss encouraging results and point to the advantages gained in using our approach.
Heterogeneously-licensed systems pose new challenges to analysts and system architects. Appropriate intellectual property rights must be available for the installed system, but without unnecessarily restricting other requirements, the system architecture, and the choice of components both initially and as it evolves. Such systems are increasingly common and important in e-business, game development, and other domains. Our semantic parameterization analysis of open-source licenses confirms that while most licenses present few roadblocks, reciprocal licenses such as the GNU General Public License produce knotty constraints that cannot be effectively managed without analysis of the system's license architecture. Our automated tool supports intellectual property requirements management and license architecture evolution. We validate our approach on an existing heterogeneously-licensed system.
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